CTSD: A Dataset for Traffic Sign Recognition in Complex Real-World Images

Traffic sign recognition (TSR) is an indispensable component for vision-based system of self-driving car. Promising results have been achieved which especially benefit from the rapid development of deep neural networks recently. However, there are few works focusing on the algorithms’ performances towards different complex conditions, such as weather and viewpoint variations. In this paper, we propose a new real-world TSR dataset, which is a dataset with several fine-grained conditions fine labeled involving weather, light condition, occlusion, distance, color fading and camera angle. Detailed and unbiased comparison results are reported about the performances of several state-of-the-arts on our proposed and five public TSR datasets. Experimental results demonstrate that current arts for TSR are still far from satisfactory especially when it comes to complex real-world cases.